A Hybrid Stochastic/Interval Approach to Transmission-Constrained Unit Commitment

This paper proposes a new transmission-constrained unit commitment method that combines the cost-efficient but computationally demanding stochastic optimization and the expensive but tractable interval optimization techniques to manage uncertainty on the expected net load. The proposed hybrid unit commitment approach applies the stochastic formulation to the initial operating hours of the optimization horizon, during which the wind forecasts are more accurate, and then switches to the interval formulation for the remaining hours. The switching time is optimized to balance the cost of unhedged uncertainty from the stochastic unit commitment against the cost of the security premium of the interval unit commitment formulation. These hybrid, stochastic, and interval formulations are compared using Monte Carlo simulations on a modified 24-bus IEEE Reliability Test System. The results demonstrate that the proposed unit commitment formulation results in the least expensive day-ahead schedule among all formulations and can be solved in the same amount of time as a full stochastic unit commitment. However, if the range of the switching time is reduced, the hybrid formulation in the parallel computing implementation outperforms the stochastic formulation in terms of computing time.

[1]  David L. Woodruff,et al.  Toward scalable, parallel progressive hedging for stochastic unit commitment , 2013, 2013 IEEE Power & Energy Society General Meeting.

[2]  Antonio J. Conejo,et al.  Economic Valuation of Reserves in Power Systems With High Penetration of Wind Power , 2009, IEEE Transactions on Power Systems.

[3]  Ross Baldick,et al.  Wind and Energy Markets: A Case Study of Texas , 2012, IEEE Systems Journal.

[4]  Jesús María Latorre Canteli,et al.  Tight and compact MILP formulation for the thermal unit commitment problem , 2013 .

[5]  Deepak Rajan,et al.  IBM Research Report Minimum Up/Down Polytopes of the Unit Commitment Problem with Start-Up Costs , 2005 .

[6]  Yongpei Guan,et al.  Unified Stochastic and Robust Unit Commitment , 2013, IEEE Transactions on Power Systems.

[7]  A. Bakirtzis,et al.  Optimal Self-Scheduling of a Thermal Producer in Short-Term Electricity Markets by MILP , 2010, IEEE Transactions on Power Systems.

[8]  A. Conejo,et al.  Market-clearing with stochastic security-part I: formulation , 2005, IEEE Transactions on Power Systems.

[9]  Adam Greenhall,et al.  Wind Scenarios for Stochastic Energy Scheduling , 2013 .

[10]  Daniel S. Kirschen,et al.  Should the spinning reserve procurement in systems with wind power generation be deterministic or probabilistic? , 2009, 2009 International Conference on Sustainable Power Generation and Supply.

[11]  S. M. Shahidehpour,et al.  Effects of ramp-rate limits on unit commitment and economic dispatch , 1993 .

[12]  J. Dupacová,et al.  Scenario reduction in stochastic programming: An approach using probability metrics , 2000 .

[13]  A. Conejo,et al.  Economic Valuation of Reserves in Power Systems With High Penetration of Wind Power , 2009 .

[14]  Matthias Lange,et al.  Analysis of the uncertainty of wind power predictions , 2003 .

[15]  Werner Römisch,et al.  Scenario Reduction Algorithms in Stochastic Programming , 2003, Comput. Optim. Appl..

[16]  F. Bouffard,et al.  Intra-hour wind power characteristics for flexible operations , 2012, 2012 IEEE Power and Energy Society General Meeting.

[17]  M. Lange On the Uncertainty of Wind Power Predictions—Analysis of the Forecast Accuracy and Statistical Distribution of Errors , 2005 .

[18]  D. Anderson,et al.  Algorithms for minimization without derivatives , 1974 .

[19]  Jian Ma,et al.  Operational Impacts of Wind Generation on California Power Systems , 2009, IEEE Transactions on Power Systems.

[20]  Daniel S. Kirschen,et al.  Effect of time resolution on unit commitment decisions in systems with high wind penetration , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[21]  Francois Bouffard,et al.  Identification of Umbrella Constraints in DC-Based Security-Constrained Optimal Power Flow , 2013, IEEE Transactions on Power Systems.

[22]  J. Watson,et al.  Multi-Stage Robust Unit Commitment Considering Wind and Demand Response Uncertainties , 2013, IEEE Transactions on Power Systems.

[23]  Daniel S. Kirschen,et al.  Comparison of state-of-the-art transmission constrained unit commitment formulations , 2013, 2013 IEEE Power & Energy Society General Meeting.

[24]  O. Alsaç,et al.  DC Power Flow Revisited , 2009, IEEE Transactions on Power Systems.

[25]  Silvano Martello,et al.  Decision Making under Uncertainty in Electricity Markets , 2015, J. Oper. Res. Soc..

[26]  M. Shahidehpour,et al.  Stochastic Security-Constrained Unit Commitment , 2007, IEEE Transactions on Power Systems.

[27]  E. Saiz-Marin,et al.  Economic Assessment of the Participation of Wind Generation in the Secondary Regulation Market , 2012, IEEE Transactions on Power Systems.

[28]  E. Litvinov,et al.  Energy and Reserve Market Designs with Explicit Consideration to Lost Opportunity Costs , 2002, IEEE Power Engineering Review.

[29]  Daniel S. Kirschen,et al.  Optimizing the spinning reserve requirements using a cost/benefit analysis , 2008, PES 2008.

[30]  G. Terrell,et al.  Iterated grid search algorithm on unimodal criteria , 1997 .

[31]  Daniel S. Kirschen,et al.  Assessing flexibility requirements in power systems , 2014 .

[32]  K. K. Kariuki,et al.  Evaluation of reliability worth and value of lost load , 1996 .

[33]  Daniel S. Kirschen,et al.  Assessing the Impact of Wind Power Generation on Operating Costs , 2010, IEEE Transactions on Smart Grid.

[34]  John W. Chinneck,et al.  Linear programming with interval coefficients , 2000, J. Oper. Res. Soc..

[35]  Daniel Kirschen,et al.  Comparison of scenario reduction techniques for the stochastic unit commitment , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[36]  Zuyi Li,et al.  Comparison of Scenario-Based and Interval Optimization Approaches to Stochastic SCUC , 2012, IEEE Transactions on Power Systems.

[37]  Anthony Papavasiliou,et al.  Multiarea Stochastic Unit Commitment for High Wind Penetration in a Transmission Constrained Network , 2013, Oper. Res..

[38]  N. Cox Statistical Models in Engineering , 1970 .

[39]  Zuyi Li,et al.  Modeling and Solution of the Large-Scale Security-Constrained Unit Commitment , 2013, IEEE Transactions on Power Systems.

[40]  Jitka Dupacová,et al.  Scenario reduction in stochastic programming , 2003, Math. Program..

[41]  Yang Wang,et al.  Unit Commitment With Volatile Node Injections by Using Interval Optimization , 2011, IEEE Transactions on Power Systems.

[42]  Xu Andy Sun,et al.  Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem , 2013, IEEE Transactions on Power Systems.

[43]  R. Belmans,et al.  Usefulness of DC power flow for active power flow analysis , 2005, IEEE Power Engineering Society General Meeting, 2005.

[44]  E. Litvinov,et al.  Energy and reserve market designs with explicit consideration to lost opportunity costs , 2002 .